An Extended Quality Function Deployment Incorporating Fuzzy Logic and GDM Under Different Preference Structures

Büyüközkan, Gülçin; Çifçi, Gizem

doi:10.1080/18756891.2015.1017379

Cited by 11 publications

(6 citation statements)

References 70 publications

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“…They describe DMs' opinions regarding possible problem alternatives in different formats. Some of these preference formats are preference orderings (Büyüközkan and Feyzio glu, 2005), (Büyüközkan, Feyzio glu and Ruan, 2007), importance degree (Büyüközkan and Feyzio glu, 2005), (Büyüközkan and Çifçi, 2013), linguistic preference relations (Büyüközkan and Çifçi, 2015), (Büyüközkan and Güleryüz, 2015), fuzzy preference relations (Dong and Zhang, 2013), multiplicative preference relations (Jiang and Xu, 2013), selected subset (Kurttila et al 2000), intuitionistic multiplicative preference relations (Xia and Xu, 2013), (Jiang and Xu, 2013) and utility functions (Dong and Zhang, 2013).…”

Section: Multiple Preference Relationsmentioning

confidence: 99%

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Integrated SWOT analysis with multiple preference relations

Büyüközkan

Ilıcak

2019

Self Cite

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Purpose SWOT (strengths, weaknesses, opportunities, threats) analysis is a powerful approach for evaluating the strengths and weaknesses of an organization with an internal perspective. The approach also takes into account the opportunities and the threats from an external point of view. These features make SWOT a commonly used approach in strategic management. The purpose of this paper is to propose an integrated SWOT analysis with multiple preference relations technique, to show the application of the proposed methodology, to prioritize the strategic factors and to present alternative strategies for ABC, a case company, which is targeting to use social media more effectively. Design/methodology/approach In this study, expert opinions are used to identify SWOT factors of ABC on social media. The obtained findings are evaluated and each factor is prioritized by means of the multiple preference relations technique. Findings The proposed evaluation model has four main groups, namely, strengths, weaknesses, opportunities, threats, under which 17 factors are identified. As a result of the evaluations, “O2: Opportunity to contact a large number of users simultaneously at affordable cost” has the highest importance level among other factors. Alternative strategies are developed based on the obtained results. Originality/value Decision-makers who have different backgrounds or ideas can state their preferences in different formats. Multiple preference relations technique is used to combine different assessments. SWOT analysis with multiple preference relations technique with a group decision-making perspective is proposed. This is the first time the method is used in the social media-related literature. With this study, the most appropriate social media strategic factors are selected for ABC and alternative strategies are determined based on the results.

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Section: Multiple Preference Relationsmentioning

confidence: 99%

“…; w kL k À Á . The IOWG vector indicates the fuzzy majority, if its weighting vector W is calculated over a fuzzy linguistic quantifier (Büyüközkan and Çifçi, 2015).…”

Section: Integratedmentioning

confidence: 99%

Integrated SWOT analysis with multiple preference relations

Büyüközkan

Ilıcak

2019

Self Cite

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“…It is important to highlight that non-reciprocal diffuse preference relationships present important advantages for the objective of this research (see [10,12,57]. In this sense, it is possible to use so-called transformation functions to convert and homogenize the different evaluation formats into the non-reciprocal fuzzy preference relations format [58,59].…”

Section: Construction and Analysis Of < X R > Modelsmentioning

confidence: 99%

Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation

Ekel,

Libório,

Ribeiro

et al. 2024

Mathematics

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This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: “How is it done?”. The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities.

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“…The TOPSIS (Hwang et al, 1981) is a widely used MCDM method that ranks the alternatives by measuring their distance from both the ideal and non-ideal solutions (Büyüközkan et al, 2017). The ideal solution is the one with the maximum benefit or the highest value for the decision-maker, while the non-ideal solution is the one with the minimum benefit or the lowest value.…”

Section: Topsismentioning

confidence: 99%

Enhancing Disaster Management through Effective Warehouse Location Selection using Linguistic Decision Making: An Integrated Approach

Büyüközkan,

Uztürk

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Effective warehouse location selection is a critical component of disaster management, as it can significantly impact the efficiency and effectiveness of disaster response efforts. This paper proposes an integrated approach based on linguistic decision making (LDM) for assessing and prioritizing key criteria for warehouse location selection in disaster management. Specifically, the 2-Tuple linguistic (2-TL) model is used to represent decision-maker's subjective opinions, and then an integrated methodology combining 2-TL-DEMATEL and 2-TL-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is employed for the analysis of the criteria and the selection of the optimal warehouse location. This approach allows decision-makers to effectively deal with subjective linguistic information and to handle uncertainty and imprecision in the decision-making process. The proposed methodology combines DEMATEL for causal relation analysis and TOPSIS for multi-criteria decision-making to identify and select the optimal warehouse location. Moreover, the paper provides a case study conducted in the Türkiye area to demonstrate the practical application of the proposed methodology. The results of the study provide an importance ranking of the key location selection criteria and reveal the causal relationships between them. The paper concludes that the proposed methodology can provide a structured and systematic approach for warehouse location selection in disaster management, and it can help decision-makers to improve the efficiency and effectiveness of disaster response efforts.

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An Extended Quality Function Deployment Incorporating Fuzzy Logic and GDM Under Different Preference Structures

Cited by 11 publications

References 70 publications

Integrated SWOT analysis with multiple preference relations

Integrated SWOT analysis with multiple preference relations

Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation

Enhancing Disaster Management through Effective Warehouse Location Selection using Linguistic Decision Making: An Integrated Approach

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